mlsanityData to 14 Oct 2026

Generating unrepresented proportions of geological facies using Generative Adversarial Networks

arXiv 2203.09639 ↗First posted 17 Mar 2022cs.LGcited by 2

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Abstract (arXiv)

In this work, we investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset. The new generated realizations with unrepresented (aka. missing) proportions are assumed to belong to the same original data distribution. Specifically, we design a conditional GANs model that can drive the generated facies toward new proportions not found in the training set. The presented study includes an investigation of various training settings and model architectures. In addition, we devised new conditioning routines for an improved generation of the missing samples. The presented numerical experiments on images of binary and multiple facies showed good geological consistency as well as strong correlation with the target conditions.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

25 of 43 references.

References · 25

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13 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysiscited by 1
  2. 2022Generation of non-stationary stochastic fields using Generative Adversarial Networkscited by 0